MLM-based pre-trained: BERT 系列 (BERT, RoBERTa, ALBERT) 1.2.1Autoregressive Language Models (ALMs): Complete the sentence given its prefix 自监督学习:从任何其他部分预测输入的任何部分 Transformer-based ALMs:由多层transformer层堆叠组成 1.2.2Masked Language Models (MLMs): Use the unmasked words to...
Pre-trained Language Model based Ranking in Baidu Search - 百度搜索中基于预训练语言模型的排名 Snowman 为发现一本好书而兴奋不已摘要 作为搜索引擎的核心,排名系统在满足用户信息需求方面发挥着至关重要的作用。最近,从预训练语言模型(PLMs)微调的神经排序器建立了最先进的排名效果。然而,直接将这些基于PLM的排序...
Then these embeddings are used as input to an autoregressive language model, which sequentially generates the output sequence tokens. These models are usually pre-trained on a large general training set and often fine-tuned for a specific task. Therefore, they are collectively called Pre-trained ...
Pre-trained Language Models Can be Fully Zero-Shot Learners Xuandong Zhao, Siqi Ouyang, Zhiguo Yu, Ming Wu, Lei Li ACL 2023|July 2023 How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data? Pre-trained lang...
a chinese minority pre-trained language model achineseminoritypre-trainedlanguagemodel的中文意思是：中国少数民族预训练语言模型。
Making Pre-trained Language Models Better Few-shot Learners 陈丹琦团队提出的一种改进GPT-3的模型,其可以扩展到任意的预训练模型上,并可以在小样本情景下更好的进行微调。 简要信息: 核心要点: Template的构建:基于T5模型生成和排序方法生成离散template; ...
natural-language-processingfew-shot-learningpre-trained-language-modelsprompt-tuningp-tuningparameter-efficient-learning UpdatedOct 6, 2022 Python Must-read Papers on Knowledge Editing for Large Language Models. reviewnatural-language-processingpapersurveyromepaper-listawsome-listpre-trained-modelpre-trained-...
分析现有的Chinese-based Pre-trained LM; 提出MacBERT(改进MLM,并提出MLM as corrector) 三、Revisit of Pre-trained Language Model BERT MLM:从输入中随机mask部分token,并预测该token; NSP:预测两个句子是否存在next关系; Whole Word Masking(WWM):mask整个词,而不是单独的word piece token; ...
Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word semantics in different contexts, the quality of word representations highly depends on word frequency, which usu...
On the other way around, we can use knowledge to improve or extend PLMs. In many knowledge-intensive downstream tasks, taking question-answering tasks as an example, the amount of knowledge learned by the pre-trained language model can be increased by adding parameters; however, it is far les...